Driven by the rapid advancement of deep learning and large-scale computing，visual perception systems have achieved remarkable progress in a wide range of domains，including autonomous driving，intelligent transportation，security surveillance，medical diagnostics，industrial inspection，and human-robot interaction. Modern vision algorithms，empowered by massive datasets and increasingly sophisticated neural architectures，are now capable of performing object detection，semantic segmentation，scene understanding，and even causal reasoning with unprecedented accuracy. Despite this rapid growth，the development of visual intelligence still faces several critical bottlenecks. Chief among these challenges are the highly imbalanced data distributions that characterize real-world environments，the scarcity of long-tail or rare-event samples essential for robustness，and the substantial human and financial cost associated with large-scale manual annotation. These factors significantly hinder the performance，safety，and generalizability of deep perception systems，especially in complex，dynamic，or safety-critical scenarios. Parallel images technology，emerging as a novel image generation and modeling methodology grounded in parallel systems theory and the artificial systems，computational experiments，and parallel execution （ACP） framework，offers a promising pathway to address these limitations. The core idea behind parallel images is to construct controllable，high-fidelity artificial scene systems that reflect the structure，behavior，physics，and semantics of their real-world counterparts. Within these artificial systems，computational experiments can be conducted at scale，allowing for the controlled generation of diverse visual data that capture variations in illumination，geometry，environmental conditions，sensor characteristics，and task-specific factors. Through the interaction and iterative feedback between virtual and real environments，parallel images establish a closed-loop mechanism of “modeling-training-feedback-optimization”，enabling perception models to evolve continuously，validate hypotheses，and improve performance under systematically generated variations. This closed-loop mechanism differentiates parallel images from traditional synthetic data generation in several important aspects. First，instead of passively producing static rendered images，parallel images emphasize dynamic parallelism，where virtual agents，environments，and tasks evolve in sync with real-world processes. Second，the approach integrates multimodal feedback，bridging visual，geometric，physical，and semantic modalities to ensure consistency and translatability across domains. Third，the framework supports scalable modeling of rare，dangerous，or expensive scenarios that are difficult or impossible to capture in real life，such as near-crash events in autonomous driving，rare diseases in medical imaging，or hazardous industrial operations. These capabilities make parallel images a powerful tool for enhancing the robustness，safety，and domain generalization of modern perception systems. This paper provides a comprehensive，systematic review of the theoretical foundations，methodological innovations，and developmental trajectory of parallel images technology. This paper begins by revisiting its roots in parallel intelligence and the ACP paradigm and detailing how artificial systems serve as controlled experimental platforms that complement real-world data collection. Then，this paper examines recent technical advances encompassing three major research directions aligned with the ACP framework： 1） multimodal data-driven virtual scene generation，which employs generative adversarial networks，diffusion models，neural radiance fields，and 3D Gaussian splatting to overcome data scarcity and annotation bottlenecks，enabling the creation of controllable，editable，and semantically consistent synthetic environments； 2） multiview feature fusion and virtual-real model transfer，aimed at addressing feature discrepancies and semantic misalignment across heterogeneous visual modalities through cross-modal alignment，multigranularity adaptive transfer，and domain-bridging strategies that enhance generalization and adaptability in hybrid virtual-real environments； 3） parallel reasoning through heterogeneous data and knowledge fusion，which integrates structured information extraction，external knowledge guidance，scene graphs，temporal logic，and large language models to advance perceptual understanding toward semantic-level reasoning and decision-making，thereby supporting continuous optimization and closed-loop evolution in complex scenes. Beyond summarizing technological developments，this paper also situates parallel images within the broader context of emerging trends in generative artificial intelligence and foundation models. With the rise of diffusion models，neural radiance fields，and large-scale multimodal models，parallel images are poised to integrate more deeply with generative simulation pipelines. This paper discusses how these innovations can strengthen the fidelity，controllability，and adaptability of artificial visual data and potentially enable new capabilities such as task-conditioned scene synthesis，human-AI cosimulation，interactive data generation，and closed-loop autonomous scenario exploration，and provides key capabilities for building general visual systems with continuous learning and feedback optimization. This paper provides crucial support for building general visual systems with continuous learning and feedback-driven optimization. Finally，this paper identifies several open challenges and future research directions that are essential for advancing the development of parallel images systems. These challenges include achieving high-quality expansion of virtual data，bridging the semantic gap between virtual and real domains，and enabling real-time，tightly coupled virtual-real interaction. Addressing these issues will require advances in intelligent generation models，self-supervised quality evaluation，unified data standards，causality-aware cross-domain alignment，and low-latency virtual-real collaboration supported by next-generation communication and sensing technologies. Solving these challenges will be critical for pushing forward the frontier of synthetic visual intelligence and unlocking the full potential of parallel images in real-world applications.